code: def integrate_gaussian(self, mean, cov):
      mean = jnp.atleast_1d(jnp.squeeze(mean))
      cov = jnp.atleast_2d(cov)
  
      if mean.shape != (self.d,):
          raise ValueError(f"mean does not have dimension {self.d}")
      if cov.shape != (self.d, self.d):
          raise ValueError(
              f"covariance does not have dimension {self.d}"
          )
  
      chol = linalg.cho_factor(self.covariance + cov)
      norm = jnp.sqrt(2 * np.pi) ** self.d * jnp.prod(jnp.diag(chol[0]))
      norm = 1.0 / norm
      return _gaussian_kernel_convolve(
          chol, norm, self.dataset, self.weights, mean
      )
  
imports: from dataclasses import dataclass
  from functools import partial
  from typing import Any
  import numpy
  import scipy.stats
  import jax.numpy
  from jax import jit, lax, random, vmap
  from jax._src.numpy.util import check_arraylike, promote_dtypes_inexact, _wraps
  from jax._src.tree_util import register_pytree_node_class
  from jax.scipy import linalg, special
label_desc: Use image node_id 9 for calling the gaussian_kde obj's underlying member method code with example usage: obj.integrate_gaussian(mean, cov) and returns: _gaussian_kernel_convolve